Biography

Keenan Jones is currently undertaking a PhD in Computer Science at the University of Kent. His research involves the application of computational methods to analyse the behaviours of cybercriminals online and his PhD focuses on the development of techniques to analyse the authorship of online texts posted by cybercriminals.

Abstract

With the rise of hacktivist groups, questions abound regarding their strange social structure and the role these groups will play as potential warriors of cyber justice, or vectors of digital harm. In order to better understand the unusual nature of these actors, we utilise computational methods to analyse the behaviours of the hacktivist group Anonymous on social media. To this end, we first used machine learning to identify a large network of more than 20,000 Anonymous affiliates on Twitter. We then utilised a combination of social network analysis and centrality measures to examine how influence is distributed throughout this Anonymous Twitter network, finding that the group is centred around a small number of highly influencer accounts. We also tracked the Anonymous network’s evolution over time, finding that the group has suffered a significant loss in membership since its peak in the early 2010s. This finding supports suggestions that the group fragmented as a result of the arrest of key members during this period. Finally, we utilised topic modelling to examine the tweet output of the top Anonymous influencers. This analysis identified a close similarity in the topics being tweeted, suggesting a unity in group interests their previous claims. These results offer new insights into the manner in which public facing hacktivist groups are organised on social media, identifying evidence of a more centralised structure within Anonymous than the group claims, reliant on a small number of key individuals.

Publications

• Jones, K., Nurse, J. R. C., & Li, S. (2020). Behind the Mask: A Computational Study of Anonymous’ Presence on Twitter. Proceedings of the International AAAI Conference on Web and Social Media, 14(1), 327-338. https://ojs.aaai.org//index.php/ICWSM/article/view/7303